English

Detection of non-self-correcting nature of information cascade

Data Analysis, Statistics and Probability 2016-07-15 v1

Abstract

We propose a method of detecting non-self-correcting information cascades in experiments in which subjects choose an option sequentially by observing the choices of previous subjects. The method uses the correlation function C(t)C(t) between the first and the t+1t+1-th subject's choices. C(t)C(t) measures the strength of the domino effect, and the limit value climtC(t)c\equiv \lim_{t\to \infty}C(t) determines whether the domino effect lasts forever (c>0)(c>0) or not (c=0)(c=0). The condition c>0c>0 is an adequate condition for a non-self-correcting system, and the probability that the majority's choice remains wrong in the limit tt\to \infty is positive. We apply the method to data from two experiments in which TT subjects answered two-choice questions: (i) general knowledge questions (Tavg=60T_{avg}=60) and (ii) urn-choice questions (T=63T=63). We find c>0c>0 for difficult questions in (i) and all cases in (ii), and the systems are not self-correcting.

Keywords

Cite

@article{arxiv.1507.07265,
  title  = {Detection of non-self-correcting nature of information cascade},
  author = {Shintaro Mori and Masafumi Hino and Masato Hisakado and Taiki Takahashi},
  journal= {arXiv preprint arXiv:1507.07265},
  year   = {2016}
}

Comments

10 pages, 4 figures